Last week
We learned 4 functions in the tidyr package:
| function | purpose |
|---|---|
gather() |
Gather variable values spread across multiple columns |
spread() |
Spread out observation values scattered across rows |
separate() |
Split one column into two or more columns |
unite() |
Collapse multiple columns into one column |
Last week
And we used these functions and a few from Week 4 to create this table:
| variable | disclosed | non-disclosed |
|---|---|---|
| c.age | 12.3 | 11.7 |
| c.female | 50.8 | 51.6 |
| p.age | 47.1 | 49.0 |
| p.female | 86.8 | 89.3 |
Objectives
Today we’ll focus on relational data. Some of today’s examples come from Wickham and Grolemund (2017). By the end of this session, you should be able to:
- Understand what makes data “relational”
- Perform inner and outer joins
Login to Duke’s Docker-ized version of RStudio Server
- Login to your instance by going to https://vm-manage.oit.duke.edu/containers and entering your NetID.
- Click on Docker
- Click on RStudio
- When RStudio loads, restart the R session (
Ctrl/Cmd+Shift+F10), clear the console (Ctrl/Cmd+L), and clear your workspace
Open your project
Is your project still open? If not, click on the project icon to load it. (Don’t create a new one.)
Download and open the template
Run the following code in your console. Change products to your preferred subfolder.
download.file("https://tinyurl.com/ybvgoovu",
destfile = "products/lab-w06.Rmd")Don’t make this mistake
One of the core principles of tidy data is that each type of observational unit is a table. Here’s the messy example from last week:
A table for every level
Relational data
We often work with this type of relational data. Sometimes the data are nested (e.g., kids within families, classrooms within schools) and we have different tables with data on each level. Other projects might involve data that just happen to be split across multiple tables that we need to bring together. In both cases, the tables are relational.
Getting relational data into R
If you have related data stored in different files, you need to load the data into R and combine the data objects. That’s what we’ll do today.
If you have a relational database, you can use RStudio to connect to the database, query/analyze the data, and only import what you need into R.
nycflights13
We’ll follow the lead of Wickham and Grolemund (2017) and use the nycflights13 package to learn the basics of relational data. Take a look at 5 tibbles: flights, airlines, airports, planes, and weather.
flights## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 533 529 4 850
## 3 2013 1 1 542 540 2 923
## 4 2013 1 1 544 545 -1 1004
## 5 2013 1 1 554 600 -6 812
## 6 2013 1 1 554 558 -4 740
## 7 2013 1 1 555 600 -5 913
## 8 2013 1 1 557 600 -3 709
## 9 2013 1 1 557 600 -3 838
## 10 2013 1 1 558 600 -2 753
## # ... with 336,766 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>nycflights13
Airline names by carrier code.
airlines## # A tibble: 16 x 2
## carrier name
## <chr> <chr>
## 1 9E Endeavor Air Inc.
## 2 AA American Airlines Inc.
## 3 AS Alaska Airlines Inc.
## 4 B6 JetBlue Airways
## 5 DL Delta Air Lines Inc.
## 6 EV ExpressJet Airlines Inc.
## 7 F9 Frontier Airlines Inc.
## 8 FL AirTran Airways Corporation
## 9 HA Hawaiian Airlines Inc.
## 10 MQ Envoy Air
## 11 OO SkyWest Airlines Inc.
## 12 UA United Air Lines Inc.
## 13 US US Airways Inc.
## 14 VX Virgin America
## 15 WN Southwest Airlines Co.
## 16 YV Mesa Airlines Inc.nycflights13
Airport details by faa airport codes.
airports## # A tibble: 1,458 x 8
## faa name lat lon alt tz
## <chr> <chr> <dbl> <dbl> <int> <dbl>
## 1 04G Lansdowne Airport 41.13047 -80.61958 1044 -5
## 2 06A Moton Field Municipal Airport 32.46057 -85.68003 264 -6
## 3 06C Schaumburg Regional 41.98934 -88.10124 801 -6
## 4 06N Randall Airport 41.43191 -74.39156 523 -5
## 5 09J Jekyll Island Airport 31.07447 -81.42778 11 -5
## 6 0A9 Elizabethton Municipal Airport 36.37122 -82.17342 1593 -5
## 7 0G6 Williams County Airport 41.46731 -84.50678 730 -5
## 8 0G7 Finger Lakes Regional Airport 42.88356 -76.78123 492 -5
## 9 0P2 Shoestring Aviation Airfield 39.79482 -76.64719 1000 -5
## 10 0S9 Jefferson County Intl 48.05381 -122.81064 108 -8
## # ... with 1,448 more rows, and 2 more variables: dst <chr>, tzone <chr>nycflights13
Plane details by tailnum.
planes## # A tibble: 3,322 x 9
## tailnum year type manufacturer model
## <chr> <int> <chr> <chr> <chr>
## 1 N10156 2004 Fixed wing multi engine EMBRAER EMB-145XR
## 2 N102UW 1998 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## 3 N103US 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## 4 N104UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## 5 N10575 2002 Fixed wing multi engine EMBRAER EMB-145LR
## 6 N105UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## 7 N107US 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## 8 N108UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## 9 N109UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## 10 N110UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## # ... with 3,312 more rows, and 4 more variables: engines <int>,
## # seats <int>, speed <int>, engine <chr>nycflights13
Weather details by airport (origin faa code) and day/hour.
weather## # A tibble: 26,130 x 15
## origin year month day hour temp dewp humid wind_dir wind_speed
## <chr> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EWR 2013 1 1 0 37.04 21.92 53.97 230 10.35702
## 2 EWR 2013 1 1 1 37.04 21.92 53.97 230 13.80936
## 3 EWR 2013 1 1 2 37.94 21.92 52.09 230 12.65858
## 4 EWR 2013 1 1 3 37.94 23.00 54.51 230 13.80936
## 5 EWR 2013 1 1 4 37.94 24.08 57.04 240 14.96014
## 6 EWR 2013 1 1 6 39.02 26.06 59.37 270 10.35702
## 7 EWR 2013 1 1 7 39.02 26.96 61.63 250 8.05546
## 8 EWR 2013 1 1 8 39.02 28.04 64.43 240 11.50780
## 9 EWR 2013 1 1 9 39.92 28.04 62.21 250 12.65858
## 10 EWR 2013 1 1 10 39.02 28.04 64.43 260 12.65858
## # ... with 26,120 more rows, and 5 more variables: wind_gust <dbl>,
## # precip <dbl>, pressure <dbl>, visib <dbl>, time_hour <dttm>See how the tibbles are related
Focus on the key relationships
flightslinks toplanesbytailnumflightslinks toairlinesbycarrierflightslinks toairportsbyoriginanddestinationflightslinks toweatherbyoriginand the time variables
Joins
When you want to combine a pair of tables, x and y, to make a bigger (wider) table, you join them.
| function | behavior |
|---|---|
inner_join() |
Keeps observations that appear in x AND y |
left_join() |
Keeps keeps all observations in x |
right_join() |
Keeps all observations in y |
full_join() |
Keeps all observations in x OR y |
left_, right_, and full_ joins are types of outer joins.
Joins
Simple example
We’ll create two data objects, x and y.
x <- tribble(
~key, ~val_x,
1, "x1",
2, "x2",
3, "x3"
)
y <- tribble(
~key, ~val_y,
1, "y1",
2, "y2",
4, "y3"
)inner_join()
The inner_join() function keeps observations that appear in x AND y, so 1 and 2. Obs 3 is dropped from x, and Obs 4 is dropped from y.
x %>%
inner_join(y, by = "key")## # A tibble: 2 x 3
## key val_x val_y
## <dbl> <chr> <chr>
## 1 1 x1 y1
## 2 2 x2 y2inner_join()
The inner_join() function keeps observations that appear in x AND y, so 1 and 2. Obs 3 is dropped from x, and Obs 4 is dropped from y.
left_join()
The left_join() function keeps keeps all observations in x. Obs 4 in y is dropped. There is no Obs 3 in y, but it appears in x.
x %>%
left_join(y, by = "key")## # A tibble: 3 x 3
## key val_x val_y
## <dbl> <chr> <chr>
## 1 1 x1 y1
## 2 2 x2 y2
## 3 3 x3 <NA>left_join()
The left_join() function keeps keeps all observations in x. Obs 4 in y is dropped. There is no Obs 3 in y, but it appears in x.
right_join()
The right_join() function keeps keeps all observations in y. Obs 3 in x is dropped. There is no Obs 4 in x, but it appears in y.
x %>%
right_join(y, by = "key")## # A tibble: 3 x 3
## key val_x val_y
## <dbl> <chr> <chr>
## 1 1 x1 y1
## 2 2 x2 y2
## 3 4 <NA> y3right_join()
The right_join() function keeps keeps all observations in y. Obs 3 in x is dropped. There is no Obs 4 in x, but it appears in y.
full_join()
The full_join() function keeps keeps all observations in x OR y. No observations are dropped.
x %>%
full_join(y, by = "key")## # A tibble: 4 x 3
## key val_x val_y
## <dbl> <chr> <chr>
## 1 1 x1 y1
## 2 2 x2 y2
## 3 3 x3 <NA>
## 4 4 <NA> y3full_join()
The full_join() function keeps keeps all observations in x OR y. No observations are dropped.
left_join() flights
Let’s say we want to join a subset of the flights data called flights2 and the planes data by the variable tailnum that exists in both tables.
flights2 %>%
left_join(planes, by = "tailnum")## # A tibble: 336,776 x 16
## year.x month day hour origin dest tailnum carrier year.y
## <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <int>
## 1 2013 1 1 5 EWR IAH N14228 UA 1999
## 2 2013 1 1 5 LGA IAH N24211 UA 1998
## 3 2013 1 1 5 JFK MIA N619AA AA 1990
## 4 2013 1 1 5 JFK BQN N804JB B6 2012
## 5 2013 1 1 6 LGA ATL N668DN DL 1991
## 6 2013 1 1 5 EWR ORD N39463 UA 2012
## 7 2013 1 1 6 EWR FLL N516JB B6 2000
## 8 2013 1 1 6 LGA IAD N829AS EV 1998
## 9 2013 1 1 6 JFK MCO N593JB B6 2004
## 10 2013 1 1 6 LGA ORD N3ALAA AA NA
## # ... with 336,766 more rows, and 7 more variables: type <chr>,
## # manufacturer <chr>, model <chr>, engines <int>, seats <int>,
## # speed <int>, engine <chr>left_join() flights
Sometimes the keys have different names. Here we join flights2 with airports by the airport code. In flights2, the airport code is in a variable called origin. In airports, the code is in the variable called faa.
flights2 %>%
left_join(airports, by = c("origin" = "faa"))## # A tibble: 336,776 x 15
## year month day hour origin dest tailnum carrier
## <int> <int> <int> <dbl> <chr> <chr> <chr> <chr>
## 1 2013 1 1 5 EWR IAH N14228 UA
## 2 2013 1 1 5 LGA IAH N24211 UA
## 3 2013 1 1 5 JFK MIA N619AA AA
## 4 2013 1 1 5 JFK BQN N804JB B6
## 5 2013 1 1 6 LGA ATL N668DN DL
## 6 2013 1 1 5 EWR ORD N39463 UA
## 7 2013 1 1 6 EWR FLL N516JB B6
## 8 2013 1 1 6 LGA IAD N829AS EV
## 9 2013 1 1 6 JFK MCO N593JB B6
## 10 2013 1 1 6 LGA ORD N3ALAA AA
## # ... with 336,766 more rows, and 7 more variables: name <chr>, lat <dbl>,
## # lon <dbl>, alt <int>, tz <dbl>, dst <chr>, tzone <chr>left_join() flights
If you don’t specify the by parameter, the default behavior is to match on all variables that appear in both tables. In this example, the following variables exist in both tables: year, month, day, hour and origin.
flights2 %>%
left_join(weather)## Joining, by = c("year", "month", "day", "hour", "origin")## # A tibble: 336,776 x 18
## year month day hour origin dest tailnum carrier temp dewp humid
## <dbl> <dbl> <int> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 2013 1 1 5 EWR IAH N14228 UA NA NA NA
## 2 2013 1 1 5 LGA IAH N24211 UA NA NA NA
## 3 2013 1 1 5 JFK MIA N619AA AA NA NA NA
## 4 2013 1 1 5 JFK BQN N804JB B6 NA NA NA
## 5 2013 1 1 6 LGA ATL N668DN DL 39.92 26.06 57.33
## 6 2013 1 1 5 EWR ORD N39463 UA NA NA NA
## 7 2013 1 1 6 EWR FLL N516JB B6 39.02 26.06 59.37
## 8 2013 1 1 6 LGA IAD N829AS EV 39.92 26.06 57.33
## 9 2013 1 1 6 JFK MCO N593JB B6 39.02 26.06 59.37
## 10 2013 1 1 6 LGA ORD N3ALAA AA 39.92 26.06 57.33
## # ... with 336,766 more rows, and 7 more variables: wind_dir <dbl>,
## # wind_speed <dbl>, wind_gust <dbl>, precip <dbl>, pressure <dbl>,
## # visib <dbl>, time_hour <dttm>Disclosure example
The template includes some code to download and load a few tables from the pediatric HIV disclosure study. Participants are nested in clinics, and clinics are nested in districts. Participants are also nested in time since this was a panel survey.
(To make things simple and prevent the possible identification of participants, the clinics and districts data are made up.)
Combine panel data
A common task is combining panel (longitudinal) datasets stored in different files. In this example, datR1 is a simplified version of the round 1 data, and datR2 contains round 2 data. We want to bring them together.
Similar, but not identical
Panel datasets should contain a lot of the same variables, but there might be some variables that only exist in some rounds.
names(datR1)## [1] "ID" "c.age" "p.knowsHIV" "clinicID"names(datR2)## [1] "ID" "p.knowsHIV"Which join function will keep all observations?
full_join() works, but…
What’s the problem?
datR1 %>%
full_join(datR2, by="ID")## ID c.age p.knowsHIV.x clinicID p.knowsHIV.y
## 1 118 9 1 2 NA
## 2 60 15 1 2 NA
## 3 311 10 1 2 NA
## 4 347 9 0 2 NA
## 5 315 10 0 2 0
## 6 99 15 1 2 NA
## 7 146 11 0 2 0
## 8 339 13 1 2 NA
## 9 228 10 1 2 NA
## 10 368 12 1 2 NA
## 11 29 10 1 2 NA
## 12 286 15 1 7 NA
## 13 220 11 0 7 1
## 14 166 13 0 7 0
## 15 278 14 0 7 0
## 16 302 13 1 7 NA
## 17 361 14 1 7 NA
## 18 120 14 1 7 NA
## 19 70 14 0 7 0
## 20 91 15 1 7 NA
## 21 337 12 1 7 NA
## 22 335 14 1 7 NA
## 23 84 15 1 7 NA
## 24 272 15 1 7 NA
## 25 300 12 0 7 1
## 26 204 15 0 7 0
## 27 164 11 1 7 NA
## 28 345 13 0 7 1
## 29 80 14 0 7 0
## 30 333 10 0 7 1
## 31 161 12 0 7 1
## 32 156 15 1 19 NA
## 33 208 13 1 19 NA
## 34 39 13 0 19 0
## 35 95 12 1 19 NA
## 36 182 13 1 19 NA
## 37 299 9 0 19 1
## 38 243 13 1 19 NA
## 39 207 12 1 19 NA
## 40 18 11 1 19 NA
## 41 358 12 1 19 NA
## 42 143 15 1 10 NA
## 43 27 11 0 10 NA
## 44 115 10 1 10 NA
## 45 219 13 0 10 0
## 46 348 12 0 10 1
## 47 119 14 1 10 NA
## 48 47 10 1 10 NA
## 49 38 12 1 10 NA
## 50 314 13 0 10 1
## 51 134 15 1 10 NA
## 52 63 14 1 10 NA
## 53 110 13 1 10 NA
## 54 155 11 0 10 0
## 55 330 14 1 10 NA
## 56 171 13 0 10 0
## 57 316 13 1 10 NA
## 58 334 11 1 10 NA
## 59 197 14 0 10 1
## 60 267 11 1 10 NA
## 61 236 13 1 10 NA
## 62 281 15 1 3 NA
## 63 241 11 1 3 NA
## 64 298 12 1 3 NA
## 65 142 11 1 3 NA
## 66 56 13 1 3 NA
## 67 127 12 1 3 NA
## 68 179 13 1 3 NA
## 69 159 12 1 3 NA
## 70 46 10 0 3 1
## 71 308 13 1 3 NA
## 72 262 14 1 3 NA
## 73 215 13 1 3 NA
## 74 310 14 1 3 NA
## 75 158 13 0 3 0
## 76 291 14 1 15 NA
## 77 148 12 0 15 0
## 78 50 11 0 15 0
## 79 8 14 1 15 NA
## 80 214 13 0 15 0
## 81 251 12 1 15 NA
## 82 320 15 1 15 NA
## 83 296 11 0 15 1
## 84 113 14 1 15 NA
## 85 15 11 0 15 0
## 86 295 14 1 20 NA
## 87 371 10 0 20 0
## 88 324 12 0 20 0
## 89 31 13 0 20 0
## 90 133 14 1 20 NA
## 91 172 10 0 20 0
## 92 85 13 1 20 NA
## 93 175 14 1 20 NA
## 94 185 9 0 20 NA
## 95 5 12 1 20 NA
## 96 45 14 1 20 NA
## 97 217 11 0 6 0
## 98 349 11 1 6 NA
## 99 67 15 1 6 NA
## 100 370 13 0 6 0
## 101 130 11 1 6 NA
## 102 187 15 0 6 0
## 103 274 14 1 6 NA
## 104 61 11 0 6 0
## 105 279 15 1 6 NA
## 106 287 9 1 6 NA
## 107 209 10 1 1 NA
## 108 128 12 0 1 0
## 109 329 10 0 1 1
## 110 276 12 0 1 0
## 111 336 10 1 1 NA
## 112 108 11 0 1 0
## 113 77 14 0 1 0
## 114 82 12 1 1 NA
## 115 121 12 0 1 0
## 116 331 15 1 1 NA
## 117 141 11 1 1 NA
## 118 90 11 0 1 0
## 119 319 10 1 1 NA
## 120 317 9 0 1 1
## 121 354 14 1 1 NA
## 122 224 11 0 1 1
## 123 365 13 1 1 NA
## 124 246 13 0 1 1
## 125 275 12 0 1 1
## 126 283 12 1 1 NA
## 127 170 12 1 1 NA
## 128 89 13 1 1 NA
## 129 78 12 0 1 1
## 130 359 12 0 14 0
## 131 34 14 1 14 NA
## 132 36 10 1 14 NA
## 133 189 12 1 14 NA
## 134 17 12 1 14 NA
## 135 30 11 1 14 NA
## 136 364 10 0 14 0
## 137 203 11 1 14 NA
## 138 4 12 1 14 NA
## 139 322 13 1 14 NA
## 140 112 10 1 14 NA
## 141 304 11 1 14 NA
## 142 74 14 1 14 NA
## 143 66 12 0 14 1
## 144 160 10 1 14 NA
## 145 104 13 1 14 NA
## 146 19 9 0 14 0
## 147 12 14 1 14 NA
## 148 312 12 1 14 NA
## 149 258 11 1 14 NA
## 150 265 14 1 14 NA
## 151 81 14 1 14 NA
## 152 293 12 1 14 NA
## 153 87 10 1 14 NA
## 154 261 11 1 14 NA
## 155 366 12 0 14 1
## 156 71 11 0 14 1
## 157 282 11 0 13 1
## 158 105 13 0 13 NA
## 159 206 10 1 13 NA
## 160 57 9 1 13 NA
## 161 147 10 1 13 NA
## 162 124 13 1 13 NA
## 163 157 10 1 13 NA
## 164 194 9 0 13 0
## 165 109 9 1 13 NA
## 166 3 12 1 13 NA
## 167 101 12 1 13 NA
## 168 59 10 1 13 NA
## 169 289 11 0 13 0
## 170 43 13 1 17 NA
## 171 253 12 0 17 0
## 172 240 13 1 17 NA
## 173 137 15 0 17 1
## 174 372 14 1 17 NA
## 175 20 12 0 17 0
## 176 290 10 1 17 NA
## 177 263 13 1 17 NA
## 178 22 10 1 17 NA
## 179 75 10 0 17 0
## 180 309 13 1 17 NA
## 181 284 11 0 17 0
## 182 162 12 1 17 NA
## 183 102 13 1 17 NA
## 184 285 12 1 18 NA
## 185 373 13 1 18 NA
## 186 92 9 0 18 0
## 187 344 13 1 18 NA
## 188 227 13 1 18 NA
## 189 186 13 0 18 0
## 190 136 11 1 18 NA
## 191 232 12 1 18 NA
## 192 297 11 0 18 NA
## 193 351 14 1 18 NA
## 194 346 13 0 18 0
## 195 149 12 1 18 NA
## 196 180 14 0 18 NA
## 197 239 13 1 18 NA
## 198 307 10 1 18 NA
## 199 23 10 1 18 NA
## 200 280 11 1 4 NA
## 201 2 10 1 4 NA
## 202 165 12 1 4 NA
## 203 114 14 1 4 NA
## 204 242 12 1 4 NA
## 205 362 14 0 4 1
## 206 177 9 0 4 0
## 207 271 9 1 4 NA
## 208 117 13 1 4 NA
## 209 235 11 1 4 NA
## 210 1 14 0 4 0
## 211 163 12 1 4 NA
## 212 255 15 1 4 NA
## 213 73 9 0 4 0
## 214 223 11 1 4 NA
## 215 252 11 1 4 NA
## 216 106 11 0 12 0
## 217 88 14 1 12 NA
## 218 51 14 1 12 NA
## 219 52 9 1 12 NA
## 220 196 13 1 12 NA
## 221 350 14 1 12 NA
## 222 191 9 1 12 NA
## 223 260 14 0 12 NA
## 224 97 14 1 12 NA
## 225 352 10 1 12 NA
## 226 360 11 1 12 NA
## 227 116 10 1 12 NA
## 228 173 12 1 9 NA
## 229 221 11 1 9 NA
## 230 76 14 0 9 1
## 231 93 15 1 9 NA
## 232 10 12 1 9 NA
## 233 257 13 1 9 NA
## 234 139 11 1 9 NA
## 235 111 9 0 9 NA
## 236 254 13 0 9 0
## 237 7 14 1 9 NA
## 238 86 13 1 9 NA
## 239 199 13 1 9 NA
## 240 168 13 1 9 NA
## 241 48 14 1 9 NA
## 242 14 12 1 9 NA
## 243 355 14 1 9 NA
## 244 126 14 1 9 NA
## 245 343 14 1 9 NA
## 246 321 11 1 9 NA
## 247 152 10 1 9 NA
## 248 72 13 0 9 1
## 249 357 10 1 9 NA
## 250 216 13 1 9 NA
## 251 181 9 0 9 NA
## 252 53 10 0 9 0
## 253 303 15 1 9 NA
## 254 369 9 0 9 0
## 255 96 9 1 9 NA
## 256 24 13 1 9 NA
## 257 288 15 1 9 NA
## 258 328 12 1 21 NA
## 259 313 11 1 21 NA
## 260 306 14 1 21 NA
## 261 237 9 0 21 1
## 262 318 11 1 21 NA
## 263 356 14 1 21 NA
## 264 247 15 0 21 0
## 265 202 10 0 21 0
## 266 13 12 0 21 0
## 267 49 10 1 21 NA
## 268 325 14 1 21 NA
## 269 122 13 1 21 NA
## 270 176 9 0 21 0
## 271 25 10 1 21 NA
## 272 129 9 1 8 NA
## 273 248 11 1 8 NA
## 274 174 13 0 8 0
## 275 269 10 0 8 0
## 276 200 13 1 8 NA
## 277 100 15 1 8 NA
## 278 54 11 1 8 NA
## 279 250 14 1 8 NA
## 280 132 13 1 8 NA
## 281 188 12 0 8 1
## 282 135 14 1 8 NA
## 283 305 12 1 8 NA
## 284 229 13 1 8 NA
## 285 184 11 1 8 NA
## 286 327 13 0 8 0
## 287 140 15 0 22 0
## 288 338 10 0 22 0
## 289 37 13 1 22 NA
## 290 98 14 0 22 1
## 291 26 11 1 22 NA
## 292 94 11 1 22 NA
## 293 183 13 1 22 NA
## 294 69 9 1 22 NA
## 295 218 14 1 22 NA
## 296 9 14 1 22 NA
## 297 205 13 0 22 1
## 298 144 12 1 11 NA
## 299 245 13 0 11 1
## 300 145 13 1 11 NA
## 301 151 15 1 11 NA
## 302 42 11 1 11 NA
## 303 62 9 1 11 NA
## 304 192 11 1 11 NA
## 305 11 15 0 11 1
## 306 332 14 1 11 NA
## 307 323 9 1 11 NA
## 308 103 14 1 11 NA
## 309 266 11 1 11 NA
## 310 341 15 1 11 NA
## 311 201 14 1 11 NA
## 312 178 13 1 11 NA
## 313 256 12 1 11 NA
## 314 238 9 1 11 NA
## 315 41 14 1 11 NA
## 316 353 12 1 11 NA
## 317 249 11 0 11 NA
## 318 211 11 0 11 0
## 319 367 11 0 11 1
## 320 225 15 1 11 NA
## 321 226 14 0 11 1
## 322 138 10 1 11 NA
## 323 326 11 1 11 NA
## 324 33 10 1 11 NA
## 325 244 9 0 11 0
## 326 264 9 0 11 1
## 327 294 14 1 11 NA
## 328 58 13 1 11 NA
## 329 273 11 0 11 0
## 330 79 15 1 11 NA
## 331 198 9 0 11 1
## 332 55 11 0 11 1
## 333 65 14 0 11 0
## 334 301 9 0 11 0
## 335 107 11 1 11 NA
## 336 83 9 1 11 NA
## 337 131 10 1 16 NA
## 338 234 12 1 16 NA
## 339 342 13 0 16 0
## 340 210 11 1 16 NA
## 341 268 13 1 16 NA
## 342 212 14 1 16 NA
## 343 123 15 1 16 NA
## 344 16 12 0 16 1
## 345 154 12 1 16 NA
## 346 44 9 0 16 1
## 347 277 11 1 16 NA
## 348 230 12 0 16 1
## 349 40 11 1 16 NA
## 350 363 12 0 16 NA
## 351 28 15 0 16 0
## 352 213 12 0 16 0
## 353 153 13 1 16 NA
## 354 195 11 0 16 1
## 355 292 15 0 16 0
## 356 125 13 1 16 NA
## 357 222 13 1 16 NA
## 358 259 10 0 16 0
## 359 340 14 1 16 NA
## 360 167 13 1 16 NA
## 361 21 14 1 16 NA
## 362 190 11 1 16 NA
## 363 35 10 0 16 0
## 364 150 13 1 16 NA
## 365 233 10 0 16 1
## 366 193 14 0 16 0
## 367 169 15 1 16 NA
## 368 6 13 1 16 NA
## 369 231 12 1 16 NA
## 370 32 13 1 16 NA
## 371 68 13 0 16 0
## 372 270 11 1 16 NAp.knowsHIV exists in both tables
And therefore appears twice with the suffix .x and .y.
datR1 %>%
full_join(datR2, by="ID")## ID c.age p.knowsHIV.x clinicID p.knowsHIV.y
## 1 118 9 1 2 NA
## 2 60 15 1 2 NA
## 3 311 10 1 2 NA
## 4 347 9 0 2 NA
## 5 315 10 0 2 0
## 6 99 15 1 2 NA
## 7 146 11 0 2 0
## 8 339 13 1 2 NA
## 9 228 10 1 2 NA
## 10 368 12 1 2 NA
## 11 29 10 1 2 NA
## 12 286 15 1 7 NA
## 13 220 11 0 7 1
## 14 166 13 0 7 0
## 15 278 14 0 7 0
## 16 302 13 1 7 NA
## 17 361 14 1 7 NA
## 18 120 14 1 7 NA
## 19 70 14 0 7 0
## 20 91 15 1 7 NA
## 21 337 12 1 7 NA
## 22 335 14 1 7 NA
## 23 84 15 1 7 NA
## 24 272 15 1 7 NA
## 25 300 12 0 7 1
## 26 204 15 0 7 0
## 27 164 11 1 7 NA
## 28 345 13 0 7 1
## 29 80 14 0 7 0
## 30 333 10 0 7 1
## 31 161 12 0 7 1
## 32 156 15 1 19 NA
## 33 208 13 1 19 NA
## 34 39 13 0 19 0
## 35 95 12 1 19 NA
## 36 182 13 1 19 NA
## 37 299 9 0 19 1
## 38 243 13 1 19 NA
## 39 207 12 1 19 NA
## 40 18 11 1 19 NA
## 41 358 12 1 19 NA
## 42 143 15 1 10 NA
## 43 27 11 0 10 NA
## 44 115 10 1 10 NA
## 45 219 13 0 10 0
## 46 348 12 0 10 1
## 47 119 14 1 10 NA
## 48 47 10 1 10 NA
## 49 38 12 1 10 NA
## 50 314 13 0 10 1
## 51 134 15 1 10 NA
## 52 63 14 1 10 NA
## 53 110 13 1 10 NA
## 54 155 11 0 10 0
## 55 330 14 1 10 NA
## 56 171 13 0 10 0
## 57 316 13 1 10 NA
## 58 334 11 1 10 NA
## 59 197 14 0 10 1
## 60 267 11 1 10 NA
## 61 236 13 1 10 NA
## 62 281 15 1 3 NA
## 63 241 11 1 3 NA
## 64 298 12 1 3 NA
## 65 142 11 1 3 NA
## 66 56 13 1 3 NA
## 67 127 12 1 3 NA
## 68 179 13 1 3 NA
## 69 159 12 1 3 NA
## 70 46 10 0 3 1
## 71 308 13 1 3 NA
## 72 262 14 1 3 NA
## 73 215 13 1 3 NA
## 74 310 14 1 3 NA
## 75 158 13 0 3 0
## 76 291 14 1 15 NA
## 77 148 12 0 15 0
## 78 50 11 0 15 0
## 79 8 14 1 15 NA
## 80 214 13 0 15 0
## 81 251 12 1 15 NA
## 82 320 15 1 15 NA
## 83 296 11 0 15 1
## 84 113 14 1 15 NA
## 85 15 11 0 15 0
## 86 295 14 1 20 NA
## 87 371 10 0 20 0
## 88 324 12 0 20 0
## 89 31 13 0 20 0
## 90 133 14 1 20 NA
## 91 172 10 0 20 0
## 92 85 13 1 20 NA
## 93 175 14 1 20 NA
## 94 185 9 0 20 NA
## 95 5 12 1 20 NA
## 96 45 14 1 20 NA
## 97 217 11 0 6 0
## 98 349 11 1 6 NA
## 99 67 15 1 6 NA
## 100 370 13 0 6 0
## 101 130 11 1 6 NA
## 102 187 15 0 6 0
## 103 274 14 1 6 NA
## 104 61 11 0 6 0
## 105 279 15 1 6 NA
## 106 287 9 1 6 NA
## 107 209 10 1 1 NA
## 108 128 12 0 1 0
## 109 329 10 0 1 1
## 110 276 12 0 1 0
## 111 336 10 1 1 NA
## 112 108 11 0 1 0
## 113 77 14 0 1 0
## 114 82 12 1 1 NA
## 115 121 12 0 1 0
## 116 331 15 1 1 NA
## 117 141 11 1 1 NA
## 118 90 11 0 1 0
## 119 319 10 1 1 NA
## 120 317 9 0 1 1
## 121 354 14 1 1 NA
## 122 224 11 0 1 1
## 123 365 13 1 1 NA
## 124 246 13 0 1 1
## 125 275 12 0 1 1
## 126 283 12 1 1 NA
## 127 170 12 1 1 NA
## 128 89 13 1 1 NA
## 129 78 12 0 1 1
## 130 359 12 0 14 0
## 131 34 14 1 14 NA
## 132 36 10 1 14 NA
## 133 189 12 1 14 NA
## 134 17 12 1 14 NA
## 135 30 11 1 14 NA
## 136 364 10 0 14 0
## 137 203 11 1 14 NA
## 138 4 12 1 14 NA
## 139 322 13 1 14 NA
## 140 112 10 1 14 NA
## 141 304 11 1 14 NA
## 142 74 14 1 14 NA
## 143 66 12 0 14 1
## 144 160 10 1 14 NA
## 145 104 13 1 14 NA
## 146 19 9 0 14 0
## 147 12 14 1 14 NA
## 148 312 12 1 14 NA
## 149 258 11 1 14 NA
## 150 265 14 1 14 NA
## 151 81 14 1 14 NA
## 152 293 12 1 14 NA
## 153 87 10 1 14 NA
## 154 261 11 1 14 NA
## 155 366 12 0 14 1
## 156 71 11 0 14 1
## 157 282 11 0 13 1
## 158 105 13 0 13 NA
## 159 206 10 1 13 NA
## 160 57 9 1 13 NA
## 161 147 10 1 13 NA
## 162 124 13 1 13 NA
## 163 157 10 1 13 NA
## 164 194 9 0 13 0
## 165 109 9 1 13 NA
## 166 3 12 1 13 NA
## 167 101 12 1 13 NA
## 168 59 10 1 13 NA
## 169 289 11 0 13 0
## 170 43 13 1 17 NA
## 171 253 12 0 17 0
## 172 240 13 1 17 NA
## 173 137 15 0 17 1
## 174 372 14 1 17 NA
## 175 20 12 0 17 0
## 176 290 10 1 17 NA
## 177 263 13 1 17 NA
## 178 22 10 1 17 NA
## 179 75 10 0 17 0
## 180 309 13 1 17 NA
## 181 284 11 0 17 0
## 182 162 12 1 17 NA
## 183 102 13 1 17 NA
## 184 285 12 1 18 NA
## 185 373 13 1 18 NA
## 186 92 9 0 18 0
## 187 344 13 1 18 NA
## 188 227 13 1 18 NA
## 189 186 13 0 18 0
## 190 136 11 1 18 NA
## 191 232 12 1 18 NA
## 192 297 11 0 18 NA
## 193 351 14 1 18 NA
## 194 346 13 0 18 0
## 195 149 12 1 18 NA
## 196 180 14 0 18 NA
## 197 239 13 1 18 NA
## 198 307 10 1 18 NA
## 199 23 10 1 18 NA
## 200 280 11 1 4 NA
## 201 2 10 1 4 NA
## 202 165 12 1 4 NA
## 203 114 14 1 4 NA
## 204 242 12 1 4 NA
## 205 362 14 0 4 1
## 206 177 9 0 4 0
## 207 271 9 1 4 NA
## 208 117 13 1 4 NA
## 209 235 11 1 4 NA
## 210 1 14 0 4 0
## 211 163 12 1 4 NA
## 212 255 15 1 4 NA
## 213 73 9 0 4 0
## 214 223 11 1 4 NA
## 215 252 11 1 4 NA
## 216 106 11 0 12 0
## 217 88 14 1 12 NA
## 218 51 14 1 12 NA
## 219 52 9 1 12 NA
## 220 196 13 1 12 NA
## 221 350 14 1 12 NA
## 222 191 9 1 12 NA
## 223 260 14 0 12 NA
## 224 97 14 1 12 NA
## 225 352 10 1 12 NA
## 226 360 11 1 12 NA
## 227 116 10 1 12 NA
## 228 173 12 1 9 NA
## 229 221 11 1 9 NA
## 230 76 14 0 9 1
## 231 93 15 1 9 NA
## 232 10 12 1 9 NA
## 233 257 13 1 9 NA
## 234 139 11 1 9 NA
## 235 111 9 0 9 NA
## 236 254 13 0 9 0
## 237 7 14 1 9 NA
## 238 86 13 1 9 NA
## 239 199 13 1 9 NA
## 240 168 13 1 9 NA
## 241 48 14 1 9 NA
## 242 14 12 1 9 NA
## 243 355 14 1 9 NA
## 244 126 14 1 9 NA
## 245 343 14 1 9 NA
## 246 321 11 1 9 NA
## 247 152 10 1 9 NA
## 248 72 13 0 9 1
## 249 357 10 1 9 NA
## 250 216 13 1 9 NA
## 251 181 9 0 9 NA
## 252 53 10 0 9 0
## 253 303 15 1 9 NA
## 254 369 9 0 9 0
## 255 96 9 1 9 NA
## 256 24 13 1 9 NA
## 257 288 15 1 9 NA
## 258 328 12 1 21 NA
## 259 313 11 1 21 NA
## 260 306 14 1 21 NA
## 261 237 9 0 21 1
## 262 318 11 1 21 NA
## 263 356 14 1 21 NA
## 264 247 15 0 21 0
## 265 202 10 0 21 0
## 266 13 12 0 21 0
## 267 49 10 1 21 NA
## 268 325 14 1 21 NA
## 269 122 13 1 21 NA
## 270 176 9 0 21 0
## 271 25 10 1 21 NA
## 272 129 9 1 8 NA
## 273 248 11 1 8 NA
## 274 174 13 0 8 0
## 275 269 10 0 8 0
## 276 200 13 1 8 NA
## 277 100 15 1 8 NA
## 278 54 11 1 8 NA
## 279 250 14 1 8 NA
## 280 132 13 1 8 NA
## 281 188 12 0 8 1
## 282 135 14 1 8 NA
## 283 305 12 1 8 NA
## 284 229 13 1 8 NA
## 285 184 11 1 8 NA
## 286 327 13 0 8 0
## 287 140 15 0 22 0
## 288 338 10 0 22 0
## 289 37 13 1 22 NA
## 290 98 14 0 22 1
## 291 26 11 1 22 NA
## 292 94 11 1 22 NA
## 293 183 13 1 22 NA
## 294 69 9 1 22 NA
## 295 218 14 1 22 NA
## 296 9 14 1 22 NA
## 297 205 13 0 22 1
## 298 144 12 1 11 NA
## 299 245 13 0 11 1
## 300 145 13 1 11 NA
## 301 151 15 1 11 NA
## 302 42 11 1 11 NA
## 303 62 9 1 11 NA
## 304 192 11 1 11 NA
## 305 11 15 0 11 1
## 306 332 14 1 11 NA
## 307 323 9 1 11 NA
## 308 103 14 1 11 NA
## 309 266 11 1 11 NA
## 310 341 15 1 11 NA
## 311 201 14 1 11 NA
## 312 178 13 1 11 NA
## 313 256 12 1 11 NA
## 314 238 9 1 11 NA
## 315 41 14 1 11 NA
## 316 353 12 1 11 NA
## 317 249 11 0 11 NA
## 318 211 11 0 11 0
## 319 367 11 0 11 1
## 320 225 15 1 11 NA
## 321 226 14 0 11 1
## 322 138 10 1 11 NA
## 323 326 11 1 11 NA
## 324 33 10 1 11 NA
## 325 244 9 0 11 0
## 326 264 9 0 11 1
## 327 294 14 1 11 NA
## 328 58 13 1 11 NA
## 329 273 11 0 11 0
## 330 79 15 1 11 NA
## 331 198 9 0 11 1
## 332 55 11 0 11 1
## 333 65 14 0 11 0
## 334 301 9 0 11 0
## 335 107 11 1 11 NA
## 336 83 9 1 11 NA
## 337 131 10 1 16 NA
## 338 234 12 1 16 NA
## 339 342 13 0 16 0
## 340 210 11 1 16 NA
## 341 268 13 1 16 NA
## 342 212 14 1 16 NA
## 343 123 15 1 16 NA
## 344 16 12 0 16 1
## 345 154 12 1 16 NA
## 346 44 9 0 16 1
## 347 277 11 1 16 NA
## 348 230 12 0 16 1
## 349 40 11 1 16 NA
## 350 363 12 0 16 NA
## 351 28 15 0 16 0
## 352 213 12 0 16 0
## 353 153 13 1 16 NA
## 354 195 11 0 16 1
## 355 292 15 0 16 0
## 356 125 13 1 16 NA
## 357 222 13 1 16 NA
## 358 259 10 0 16 0
## 359 340 14 1 16 NA
## 360 167 13 1 16 NA
## 361 21 14 1 16 NA
## 362 190 11 1 16 NA
## 363 35 10 0 16 0
## 364 150 13 1 16 NA
## 365 233 10 0 16 1
## 366 193 14 0 16 0
## 367 169 15 1 16 NA
## 368 6 13 1 16 NA
## 369 231 12 1 16 NA
## 370 32 13 1 16 NA
## 371 68 13 0 16 0
## 372 270 11 1 16 NAUse the suffix argument
datR1 %>%
full_join(datR2,
by = "ID",
suffix = c(".r1", ".r2"))## ID c.age p.knowsHIV.r1 clinicID p.knowsHIV.r2
## 1 118 9 1 2 NA
## 2 60 15 1 2 NA
## 3 311 10 1 2 NA
## 4 347 9 0 2 NA
## 5 315 10 0 2 0
## 6 99 15 1 2 NA
## 7 146 11 0 2 0
## 8 339 13 1 2 NA
## 9 228 10 1 2 NA
## 10 368 12 1 2 NA
## 11 29 10 1 2 NA
## 12 286 15 1 7 NA
## 13 220 11 0 7 1
## 14 166 13 0 7 0
## 15 278 14 0 7 0
## 16 302 13 1 7 NA
## 17 361 14 1 7 NA
## 18 120 14 1 7 NA
## 19 70 14 0 7 0
## 20 91 15 1 7 NA
## 21 337 12 1 7 NA
## 22 335 14 1 7 NA
## 23 84 15 1 7 NA
## 24 272 15 1 7 NA
## 25 300 12 0 7 1
## 26 204 15 0 7 0
## 27 164 11 1 7 NA
## 28 345 13 0 7 1
## 29 80 14 0 7 0
## 30 333 10 0 7 1
## 31 161 12 0 7 1
## 32 156 15 1 19 NA
## 33 208 13 1 19 NA
## 34 39 13 0 19 0
## 35 95 12 1 19 NA
## 36 182 13 1 19 NA
## 37 299 9 0 19 1
## 38 243 13 1 19 NA
## 39 207 12 1 19 NA
## 40 18 11 1 19 NA
## 41 358 12 1 19 NA
## 42 143 15 1 10 NA
## 43 27 11 0 10 NA
## 44 115 10 1 10 NA
## 45 219 13 0 10 0
## 46 348 12 0 10 1
## 47 119 14 1 10 NA
## 48 47 10 1 10 NA
## 49 38 12 1 10 NA
## 50 314 13 0 10 1
## 51 134 15 1 10 NA
## 52 63 14 1 10 NA
## 53 110 13 1 10 NA
## 54 155 11 0 10 0
## 55 330 14 1 10 NA
## 56 171 13 0 10 0
## 57 316 13 1 10 NA
## 58 334 11 1 10 NA
## 59 197 14 0 10 1
## 60 267 11 1 10 NA
## 61 236 13 1 10 NA
## 62 281 15 1 3 NA
## 63 241 11 1 3 NA
## 64 298 12 1 3 NA
## 65 142 11 1 3 NA
## 66 56 13 1 3 NA
## 67 127 12 1 3 NA
## 68 179 13 1 3 NA
## 69 159 12 1 3 NA
## 70 46 10 0 3 1
## 71 308 13 1 3 NA
## 72 262 14 1 3 NA
## 73 215 13 1 3 NA
## 74 310 14 1 3 NA
## 75 158 13 0 3 0
## 76 291 14 1 15 NA
## 77 148 12 0 15 0
## 78 50 11 0 15 0
## 79 8 14 1 15 NA
## 80 214 13 0 15 0
## 81 251 12 1 15 NA
## 82 320 15 1 15 NA
## 83 296 11 0 15 1
## 84 113 14 1 15 NA
## 85 15 11 0 15 0
## 86 295 14 1 20 NA
## 87 371 10 0 20 0
## 88 324 12 0 20 0
## 89 31 13 0 20 0
## 90 133 14 1 20 NA
## 91 172 10 0 20 0
## 92 85 13 1 20 NA
## 93 175 14 1 20 NA
## 94 185 9 0 20 NA
## 95 5 12 1 20 NA
## 96 45 14 1 20 NA
## 97 217 11 0 6 0
## 98 349 11 1 6 NA
## 99 67 15 1 6 NA
## 100 370 13 0 6 0
## 101 130 11 1 6 NA
## 102 187 15 0 6 0
## 103 274 14 1 6 NA
## 104 61 11 0 6 0
## 105 279 15 1 6 NA
## 106 287 9 1 6 NA
## 107 209 10 1 1 NA
## 108 128 12 0 1 0
## 109 329 10 0 1 1
## 110 276 12 0 1 0
## 111 336 10 1 1 NA
## 112 108 11 0 1 0
## 113 77 14 0 1 0
## 114 82 12 1 1 NA
## 115 121 12 0 1 0
## 116 331 15 1 1 NA
## 117 141 11 1 1 NA
## 118 90 11 0 1 0
## 119 319 10 1 1 NA
## 120 317 9 0 1 1
## 121 354 14 1 1 NA
## 122 224 11 0 1 1
## 123 365 13 1 1 NA
## 124 246 13 0 1 1
## 125 275 12 0 1 1
## 126 283 12 1 1 NA
## 127 170 12 1 1 NA
## 128 89 13 1 1 NA
## 129 78 12 0 1 1
## 130 359 12 0 14 0
## 131 34 14 1 14 NA
## 132 36 10 1 14 NA
## 133 189 12 1 14 NA
## 134 17 12 1 14 NA
## 135 30 11 1 14 NA
## 136 364 10 0 14 0
## 137 203 11 1 14 NA
## 138 4 12 1 14 NA
## 139 322 13 1 14 NA
## 140 112 10 1 14 NA
## 141 304 11 1 14 NA
## 142 74 14 1 14 NA
## 143 66 12 0 14 1
## 144 160 10 1 14 NA
## 145 104 13 1 14 NA
## 146 19 9 0 14 0
## 147 12 14 1 14 NA
## 148 312 12 1 14 NA
## 149 258 11 1 14 NA
## 150 265 14 1 14 NA
## 151 81 14 1 14 NA
## 152 293 12 1 14 NA
## 153 87 10 1 14 NA
## 154 261 11 1 14 NA
## 155 366 12 0 14 1
## 156 71 11 0 14 1
## 157 282 11 0 13 1
## 158 105 13 0 13 NA
## 159 206 10 1 13 NA
## 160 57 9 1 13 NA
## 161 147 10 1 13 NA
## 162 124 13 1 13 NA
## 163 157 10 1 13 NA
## 164 194 9 0 13 0
## 165 109 9 1 13 NA
## 166 3 12 1 13 NA
## 167 101 12 1 13 NA
## 168 59 10 1 13 NA
## 169 289 11 0 13 0
## 170 43 13 1 17 NA
## 171 253 12 0 17 0
## 172 240 13 1 17 NA
## 173 137 15 0 17 1
## 174 372 14 1 17 NA
## 175 20 12 0 17 0
## 176 290 10 1 17 NA
## 177 263 13 1 17 NA
## 178 22 10 1 17 NA
## 179 75 10 0 17 0
## 180 309 13 1 17 NA
## 181 284 11 0 17 0
## 182 162 12 1 17 NA
## 183 102 13 1 17 NA
## 184 285 12 1 18 NA
## 185 373 13 1 18 NA
## 186 92 9 0 18 0
## 187 344 13 1 18 NA
## 188 227 13 1 18 NA
## 189 186 13 0 18 0
## 190 136 11 1 18 NA
## 191 232 12 1 18 NA
## 192 297 11 0 18 NA
## 193 351 14 1 18 NA
## 194 346 13 0 18 0
## 195 149 12 1 18 NA
## 196 180 14 0 18 NA
## 197 239 13 1 18 NA
## 198 307 10 1 18 NA
## 199 23 10 1 18 NA
## 200 280 11 1 4 NA
## 201 2 10 1 4 NA
## 202 165 12 1 4 NA
## 203 114 14 1 4 NA
## 204 242 12 1 4 NA
## 205 362 14 0 4 1
## 206 177 9 0 4 0
## 207 271 9 1 4 NA
## 208 117 13 1 4 NA
## 209 235 11 1 4 NA
## 210 1 14 0 4 0
## 211 163 12 1 4 NA
## 212 255 15 1 4 NA
## 213 73 9 0 4 0
## 214 223 11 1 4 NA
## 215 252 11 1 4 NA
## 216 106 11 0 12 0
## 217 88 14 1 12 NA
## 218 51 14 1 12 NA
## 219 52 9 1 12 NA
## 220 196 13 1 12 NA
## 221 350 14 1 12 NA
## 222 191 9 1 12 NA
## 223 260 14 0 12 NA
## 224 97 14 1 12 NA
## 225 352 10 1 12 NA
## 226 360 11 1 12 NA
## 227 116 10 1 12 NA
## 228 173 12 1 9 NA
## 229 221 11 1 9 NA
## 230 76 14 0 9 1
## 231 93 15 1 9 NA
## 232 10 12 1 9 NA
## 233 257 13 1 9 NA
## 234 139 11 1 9 NA
## 235 111 9 0 9 NA
## 236 254 13 0 9 0
## 237 7 14 1 9 NA
## 238 86 13 1 9 NA
## 239 199 13 1 9 NA
## 240 168 13 1 9 NA
## 241 48 14 1 9 NA
## 242 14 12 1 9 NA
## 243 355 14 1 9 NA
## 244 126 14 1 9 NA
## 245 343 14 1 9 NA
## 246 321 11 1 9 NA
## 247 152 10 1 9 NA
## 248 72 13 0 9 1
## 249 357 10 1 9 NA
## 250 216 13 1 9 NA
## 251 181 9 0 9 NA
## 252 53 10 0 9 0
## 253 303 15 1 9 NA
## 254 369 9 0 9 0
## 255 96 9 1 9 NA
## 256 24 13 1 9 NA
## 257 288 15 1 9 NA
## 258 328 12 1 21 NA
## 259 313 11 1 21 NA
## 260 306 14 1 21 NA
## 261 237 9 0 21 1
## 262 318 11 1 21 NA
## 263 356 14 1 21 NA
## 264 247 15 0 21 0
## 265 202 10 0 21 0
## 266 13 12 0 21 0
## 267 49 10 1 21 NA
## 268 325 14 1 21 NA
## 269 122 13 1 21 NA
## 270 176 9 0 21 0
## 271 25 10 1 21 NA
## 272 129 9 1 8 NA
## 273 248 11 1 8 NA
## 274 174 13 0 8 0
## 275 269 10 0 8 0
## 276 200 13 1 8 NA
## 277 100 15 1 8 NA
## 278 54 11 1 8 NA
## 279 250 14 1 8 NA
## 280 132 13 1 8 NA
## 281 188 12 0 8 1
## 282 135 14 1 8 NA
## 283 305 12 1 8 NA
## 284 229 13 1 8 NA
## 285 184 11 1 8 NA
## 286 327 13 0 8 0
## 287 140 15 0 22 0
## 288 338 10 0 22 0
## 289 37 13 1 22 NA
## 290 98 14 0 22 1
## 291 26 11 1 22 NA
## 292 94 11 1 22 NA
## 293 183 13 1 22 NA
## 294 69 9 1 22 NA
## 295 218 14 1 22 NA
## 296 9 14 1 22 NA
## 297 205 13 0 22 1
## 298 144 12 1 11 NA
## 299 245 13 0 11 1
## 300 145 13 1 11 NA
## 301 151 15 1 11 NA
## 302 42 11 1 11 NA
## 303 62 9 1 11 NA
## 304 192 11 1 11 NA
## 305 11 15 0 11 1
## 306 332 14 1 11 NA
## 307 323 9 1 11 NA
## 308 103 14 1 11 NA
## 309 266 11 1 11 NA
## 310 341 15 1 11 NA
## 311 201 14 1 11 NA
## 312 178 13 1 11 NA
## 313 256 12 1 11 NA
## 314 238 9 1 11 NA
## 315 41 14 1 11 NA
## 316 353 12 1 11 NA
## 317 249 11 0 11 NA
## 318 211 11 0 11 0
## 319 367 11 0 11 1
## 320 225 15 1 11 NA
## 321 226 14 0 11 1
## 322 138 10 1 11 NA
## 323 326 11 1 11 NA
## 324 33 10 1 11 NA
## 325 244 9 0 11 0
## 326 264 9 0 11 1
## 327 294 14 1 11 NA
## 328 58 13 1 11 NA
## 329 273 11 0 11 0
## 330 79 15 1 11 NA
## 331 198 9 0 11 1
## 332 55 11 0 11 1
## 333 65 14 0 11 0
## 334 301 9 0 11 0
## 335 107 11 1 11 NA
## 336 83 9 1 11 NA
## 337 131 10 1 16 NA
## 338 234 12 1 16 NA
## 339 342 13 0 16 0
## 340 210 11 1 16 NA
## 341 268 13 1 16 NA
## 342 212 14 1 16 NA
## 343 123 15 1 16 NA
## 344 16 12 0 16 1
## 345 154 12 1 16 NA
## 346 44 9 0 16 1
## 347 277 11 1 16 NA
## 348 230 12 0 16 1
## 349 40 11 1 16 NA
## 350 363 12 0 16 NA
## 351 28 15 0 16 0
## 352 213 12 0 16 0
## 353 153 13 1 16 NA
## 354 195 11 0 16 1
## 355 292 15 0 16 0
## 356 125 13 1 16 NA
## 357 222 13 1 16 NA
## 358 259 10 0 16 0
## 359 340 14 1 16 NA
## 360 167 13 1 16 NA
## 361 21 14 1 16 NA
## 362 190 11 1 16 NA
## 363 35 10 0 16 0
## 364 150 13 1 16 NA
## 365 233 10 0 16 1
## 366 193 14 0 16 0
## 367 169 15 1 16 NA
## 368 6 13 1 16 NA
## 369 231 12 1 16 NA
## 370 32 13 1 16 NA
## 371 68 13 0 16 0
## 372 270 11 1 16 NAA brief detour
My attempt
r1 <- data.frame(id=c(1, 2, 3),
v1=c(1, 1, 0),
v2=c(0, 1, 1),
round=c(1, 1, 1))
r2 <- data.frame(id=c(1, 2, 3),
v2=c(1, 0, 0),
round=c(2, 2, 2))
r12 <- bind_rows(r1, r2)
r12w <- reshape(r12,
timevar = "round",
v.names = "v2",
idvar = "id",
direction = "wide")## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : some constant variables (v1) are really varying r12w## id v1 v2.1 v2.2
## 1 1 1 0 1
## 2 2 1 1 0
## 3 3 0 1 0Some reactions
A promising idea
r12 %>%
mutate(v1 = replace(v1, is.na(v1), v1[!is.na(v1)])) %>%
mutate(round = paste0("v2.", round)) %>%
spread(round, v2)## id v1 v2.1 v2.2
## 1 1 1 0 1
## 2 2 1 1 0
## 3 3 0 1 0A new idea
nm1 <- setdiff(names(r1), names(r2))
r2[nm1] <- r1[nm1]
bind_rows(r1, r2) %>%
mutate(round = paste0("v2.", round)) %>%
spread(round, v2)## id v1 v2.1 v2.2
## 1 1 1 0 1
## 2 2 1 1 0
## 3 3 0 1 0Another wrinkle
An updated example
The Answer!
The Answer!
full_join(r1, r2, by = "id",
suffix = c(".1", ".2")) %>%
select(-starts_with("round"))## id v1 v2.1 v2.2
## 1 1 1 0 1
## 2 2 1 1 0
## 3 3 0 1 0
## 4 4 0 1 NANow join individuals to clinics
Now join individuals to clinics
datR1 %>%
full_join(datR2,
by = "ID",
suffix = c(".r1", ".r2")) %>%
left_join(clinics, by="clinicID")## ID c.age p.knowsHIV.r1 clinicID p.knowsHIV.r2 district public
## 1 118 9 1 2 NA A 1
## 2 60 15 1 2 NA A 1
## 3 311 10 1 2 NA A 1
## 4 347 9 0 2 NA A 1
## 5 315 10 0 2 0 A 1
## 6 99 15 1 2 NA A 1
## 7 146 11 0 2 0 A 1
## 8 339 13 1 2 NA A 1
## 9 228 10 1 2 NA A 1
## 10 368 12 1 2 NA A 1
## 11 29 10 1 2 NA A 1
## 12 286 15 1 7 NA A 1
## 13 220 11 0 7 1 A 1
## 14 166 13 0 7 0 A 1
## 15 278 14 0 7 0 A 1
## 16 302 13 1 7 NA A 1
## 17 361 14 1 7 NA A 1
## 18 120 14 1 7 NA A 1
## 19 70 14 0 7 0 A 1
## 20 91 15 1 7 NA A 1
## 21 337 12 1 7 NA A 1
## 22 335 14 1 7 NA A 1
## 23 84 15 1 7 NA A 1
## 24 272 15 1 7 NA A 1
## 25 300 12 0 7 1 A 1
## 26 204 15 0 7 0 A 1
## 27 164 11 1 7 NA A 1
## 28 345 13 0 7 1 A 1
## 29 80 14 0 7 0 A 1
## 30 333 10 0 7 1 A 1
## 31 161 12 0 7 1 A 1
## 32 156 15 1 19 NA A 1
## 33 208 13 1 19 NA A 1
## 34 39 13 0 19 0 A 1
## 35 95 12 1 19 NA A 1
## 36 182 13 1 19 NA A 1
## 37 299 9 0 19 1 A 1
## 38 243 13 1 19 NA A 1
## 39 207 12 1 19 NA A 1
## 40 18 11 1 19 NA A 1
## 41 358 12 1 19 NA A 1
## 42 143 15 1 10 NA A 1
## 43 27 11 0 10 NA A 1
## 44 115 10 1 10 NA A 1
## 45 219 13 0 10 0 A 1
## 46 348 12 0 10 1 A 1
## 47 119 14 1 10 NA A 1
## 48 47 10 1 10 NA A 1
## 49 38 12 1 10 NA A 1
## 50 314 13 0 10 1 A 1
## 51 134 15 1 10 NA A 1
## 52 63 14 1 10 NA A 1
## 53 110 13 1 10 NA A 1
## 54 155 11 0 10 0 A 1
## 55 330 14 1 10 NA A 1
## 56 171 13 0 10 0 A 1
## 57 316 13 1 10 NA A 1
## 58 334 11 1 10 NA A 1
## 59 197 14 0 10 1 A 1
## 60 267 11 1 10 NA A 1
## 61 236 13 1 10 NA A 1
## 62 281 15 1 3 NA A 1
## 63 241 11 1 3 NA A 1
## 64 298 12 1 3 NA A 1
## 65 142 11 1 3 NA A 1
## 66 56 13 1 3 NA A 1
## 67 127 12 1 3 NA A 1
## 68 179 13 1 3 NA A 1
## 69 159 12 1 3 NA A 1
## 70 46 10 0 3 1 A 1
## 71 308 13 1 3 NA A 1
## 72 262 14 1 3 NA A 1
## 73 215 13 1 3 NA A 1
## 74 310 14 1 3 NA A 1
## 75 158 13 0 3 0 A 1
## 76 291 14 1 15 NA A 1
## 77 148 12 0 15 0 A 1
## 78 50 11 0 15 0 A 1
## 79 8 14 1 15 NA A 1
## 80 214 13 0 15 0 A 1
## 81 251 12 1 15 NA A 1
## 82 320 15 1 15 NA A 1
## 83 296 11 0 15 1 A 1
## 84 113 14 1 15 NA A 1
## 85 15 11 0 15 0 A 1
## 86 295 14 1 20 NA A 0
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## 336 83 9 1 11 NA B 0
## 337 131 10 1 16 NA B 1
## 338 234 12 1 16 NA B 1
## 339 342 13 0 16 0 B 1
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## 344 16 12 0 16 1 B 1
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## 346 44 9 0 16 1 B 1
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## 348 230 12 0 16 1 B 1
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## 350 363 12 0 16 NA B 1
## 351 28 15 0 16 0 B 1
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## 354 195 11 0 16 1 B 1
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## 356 125 13 1 16 NA B 1
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## 358 259 10 0 16 0 B 1
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## 361 21 14 1 16 NA B 1
## 362 190 11 1 16 NA B 1
## 363 35 10 0 16 0 B 1
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## 365 233 10 0 16 1 B 1
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## 370 32 13 1 16 NA B 1
## 371 68 13 0 16 0 B 1
## 372 270 11 1 16 NA B 1Now join individuals/clinics to districts
datR1 %>%
full_join(datR2,
by = "ID",
suffix = c(".r1", ".r2")) %>%
left_join(clinics, by="clinicID") %>%
left_join(districts, by="district")## ID c.age p.knowsHIV.r1 clinicID p.knowsHIV.r2 district public pop
## 1 118 9 1 2 NA A 1 150000
## 2 60 15 1 2 NA A 1 150000
## 3 311 10 1 2 NA A 1 150000
## 4 347 9 0 2 NA A 1 150000
## 5 315 10 0 2 0 A 1 150000
## 6 99 15 1 2 NA A 1 150000
## 7 146 11 0 2 0 A 1 150000
## 8 339 13 1 2 NA A 1 150000
## 9 228 10 1 2 NA A 1 150000
## 10 368 12 1 2 NA A 1 150000
## 11 29 10 1 2 NA A 1 150000
## 12 286 15 1 7 NA A 1 150000
## 13 220 11 0 7 1 A 1 150000
## 14 166 13 0 7 0 A 1 150000
## 15 278 14 0 7 0 A 1 150000
## 16 302 13 1 7 NA A 1 150000
## 17 361 14 1 7 NA A 1 150000
## 18 120 14 1 7 NA A 1 150000
## 19 70 14 0 7 0 A 1 150000
## 20 91 15 1 7 NA A 1 150000
## 21 337 12 1 7 NA A 1 150000
## 22 335 14 1 7 NA A 1 150000
## 23 84 15 1 7 NA A 1 150000
## 24 272 15 1 7 NA A 1 150000
## 25 300 12 0 7 1 A 1 150000
## 26 204 15 0 7 0 A 1 150000
## 27 164 11 1 7 NA A 1 150000
## 28 345 13 0 7 1 A 1 150000
## 29 80 14 0 7 0 A 1 150000
## 30 333 10 0 7 1 A 1 150000
## 31 161 12 0 7 1 A 1 150000
## 32 156 15 1 19 NA A 1 150000
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## 34 39 13 0 19 0 A 1 150000
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## 37 299 9 0 19 1 A 1 150000
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## 40 18 11 1 19 NA A 1 150000
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## 70 46 10 0 3 1 A 1 150000
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## 157 282 11 0 13 1 B 1 250000
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## 337 131 10 1 16 NA B 1 250000
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## 339 342 13 0 16 0 B 1 250000
## 340 210 11 1 16 NA B 1 250000
## 341 268 13 1 16 NA B 1 250000
## 342 212 14 1 16 NA B 1 250000
## 343 123 15 1 16 NA B 1 250000
## 344 16 12 0 16 1 B 1 250000
## 345 154 12 1 16 NA B 1 250000
## 346 44 9 0 16 1 B 1 250000
## 347 277 11 1 16 NA B 1 250000
## 348 230 12 0 16 1 B 1 250000
## 349 40 11 1 16 NA B 1 250000
## 350 363 12 0 16 NA B 1 250000
## 351 28 15 0 16 0 B 1 250000
## 352 213 12 0 16 0 B 1 250000
## 353 153 13 1 16 NA B 1 250000
## 354 195 11 0 16 1 B 1 250000
## 355 292 15 0 16 0 B 1 250000
## 356 125 13 1 16 NA B 1 250000
## 357 222 13 1 16 NA B 1 250000
## 358 259 10 0 16 0 B 1 250000
## 359 340 14 1 16 NA B 1 250000
## 360 167 13 1 16 NA B 1 250000
## 361 21 14 1 16 NA B 1 250000
## 362 190 11 1 16 NA B 1 250000
## 363 35 10 0 16 0 B 1 250000
## 364 150 13 1 16 NA B 1 250000
## 365 233 10 0 16 1 B 1 250000
## 366 193 14 0 16 0 B 1 250000
## 367 169 15 1 16 NA B 1 250000
## 368 6 13 1 16 NA B 1 250000
## 369 231 12 1 16 NA B 1 250000
## 370 32 13 1 16 NA B 1 250000
## 371 68 13 0 16 0 B 1 250000
## 372 270 11 1 16 NA B 1 250000References
Wickham, Hadley, and Garrett Grolemund. 2017. R for Data Science. O’Reilly. http://r4ds.had.co.nz/.